Epilepsy is a neurological disorder that causes sudden seizures due to abnormal excitation of neurons in the brain. Approximately 30 % of patients cannot control their seizures using medication. In addition, since seizures can occur anywhere and at any time, caregivers must always be with the patient. Various researchers have developed seizure detection methods using multichannel EEG to improve the quality of life of patients and caregivers. However, the large size of the measurement device impedes transportation. We believe that a portable measurement device with a small number of channels is suitable for detecting seizures in daily life. Therefore, we need a system that can detect seizures using a small number of channels. The purpose of this research is to develop a seizure detection algorithm using a single-channel frontal EEG and to confirm its basic performance. We used EEG signals from a single electrode position (Fp1-F7, Fp2-F8), which is a bipolar derivation of the frontal region. We segmented the EEG using a 2 s sliding window with 50 % overlap and converted the segments into images. After preprocessing, we fine-tuned ResNet18, pre-trained on ImageNet, and developed an ensemble classification method. In the experiments with 10 epileptic patients (3 - 19 years old) registered in the CHB-MIT scalp EEG database, the results showed that the average sensitivity was 88.73 %, the average specificity was 98.98 %, and the average detection latency time was 7.39 s. In conclusion, the developed algorithm was validated as sufficiently accurate to detect epileptic seizures.Clinical Relevance- This establishes an image recognition algorithm that can detect epileptic seizures using a single- channel frontal EEG.